resourcefindingTeam.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8xgNDTtEIezi"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import random\n",
        "from tqdm.notebook import tqdm\n",
        "import time\n",
        "import sys\n",
        "import matplotlib.pyplot as plt\n",
        "from IPython.display import display, clear_output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "# Make some points\n",
        "grid=[[3,0,0,0,0,0,0,0,0,3]\n",
        "                ,[0,0,0,0,0,0,2,1,1,0]\n",
        "                ,[0,0,0,0,0,0,1,1,1,0]\n",
        "                ,[0,0,0,0,0,0,1,1,1,0]\n",
        "                ,[0,0,0,0,0,0,0,0,0,0]\n",
        "                ,[0,0,1,1,1,1,1,0,0,0]\n",
        "                ,[0,0,1,1,1,1,1,0,0,0]\n",
        "                ,[0,0,1,1,1,2,2,2,0,0]\n",
        "                ,[0,0,1,1,1,2,2,2,0,0]\n",
        "                ,[3,0,1,1,1,2,2,2,0,3]]\n",
        "\n",
        "# Make the plot\n",
        "plt.hist2d(grid)\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "42QN592qN6Uc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "AteRqY40I8TQ"
      },
      "outputs": [],
      "source": [
        "class OilF:\n",
        "  def __init__(self):\n",
        "      self.grid=[[3,0,0,0,0,0,0,0,0,3]\n",
        "                ,[0,0,0,0,0,0,2,1,1,0]\n",
        "                ,[0,0,0,0,0,0,1,1,1,0]\n",
        "                ,[0,0,0,0,0,0,1,1,1,0]\n",
        "                ,[0,0,0,0,0,0,0,0,0,0]\n",
        "                ,[0,0,1,1,1,1,1,0,0,0]\n",
        "                ,[0,0,1,1,1,1,1,0,0,0]\n",
        "                ,[0,0,1,1,1,2,2,2,0,0]\n",
        "                ,[0,0,1,1,1,2,2,2,0,0]\n",
        "                ,[3,0,1,1,1,2,2,2,0,3]]\n",
        "      self.oilF={(2,7):[2,8],(7,4):[3,16]}\n",
        "      self.obs={(1,6):1,(8,6):2}\n",
        "      self.actions=[1,2,3,4,5,6]\n",
        "      self.states={}\n",
        "      self.numStates={}\n",
        "      self.robotXs=0 #random.randint(0,9)  \n",
        "      self.robotYs=0 #random.randint(0,9) \n",
        "      self.robotLoad=0\n",
        "      self.reward=0\n",
        "      self.done=False\n",
        "      self.dilv={(0,0),(0,9),(9,0),(9,9)}\n",
        "      self.count=[0,0,0,0,0,0]\n",
        "\n",
        "      x=0\n",
        "      self.maxload=3\n",
        "      self.rangX=10\n",
        "      self.rangY=10\n",
        "      self.oilF1C=3\n",
        "      self.oilF2C=4\n",
        "      for robotL in range(self.maxload):\n",
        "        for robotX in range(self.rangX):\n",
        "          for robotY in range(self.rangY):\n",
        "            for oilF1 in range(self.oilF1C):\n",
        "              for oilF2 in range(self.oilF2C):\n",
        "                self.states[x]=robotL,robotX,robotY,oilF1,oilF2\n",
        "                self.numStates[(robotL,robotX,robotY,oilF1,oilF2)]=x\n",
        "                x+=1\n",
        "\n",
        "    \n",
        "\n",
        "  def reset(self):\n",
        "    self.__init__()\n",
        "    return 0\n",
        "\n",
        "  def actionSpaceSample(self):\n",
        "    return random.randint(0,5)   \n",
        "\n",
        "  def step(self,action):\n",
        "    self.reward=0\n",
        "    if action ==0:\n",
        "      self.count[0]+=1\n",
        "      self.reward-=2\n",
        "      if self.robotYs!=0:\n",
        "        self.robotYs-=1\n",
        "    elif action == 1:\n",
        "      self.count[1]+=1\n",
        "      self.reward-=2\n",
        "      if self.robotYs!=9:\n",
        "        self.robotYs+=1 \n",
        "    elif action == 2:\n",
        "      self.count[2]+=1\n",
        "      self.reward-=2\n",
        "      if self.robotXs!=0:\n",
        "        self.robotXs-=1\n",
        "    elif action == 3:\n",
        "      self.count[3]+=1\n",
        "      self.reward-=2\n",
        "      if self.robotXs!=9:\n",
        "        self.robotXs+=1\n",
        "    elif action == 4:\n",
        "      self.count[4]+=1\n",
        "      flage=True\n",
        "      for o in self.obs:\n",
        "        if abs(self.robotXs-o[0])<self.obs[o] and abs(self.robotYs-o[1])<self.obs[o]:\n",
        "          self.reward-=5\n",
        "          flage=False\n",
        "      if  flage:\n",
        "        for f in self.oilF:\n",
        "          if abs(self.robotXs-f[0])<self.oilF[f][0] and abs(self.robotYs-f[1])<self.oilF[f][0]:\n",
        "              if self.robotLoad < self.maxload-1:\n",
        "                self.reward+=5\n",
        "                self.robotLoad+=1\n",
        "                if self.oilF[f][0]!=0:\n",
        "                  self.oilF[f][1]-=1\n",
        "                if self.oilF[f][1]<=0:\n",
        "                  if self.oilF[f][0]==1:\n",
        "                    self.oilF[f][0]=0\n",
        "                    self.oilF[f][1]=0\n",
        "                  elif self.oilF[f][0]==2:\n",
        "                    self.oilF[f][0]=1\n",
        "                    self.oilF[f][1]=0\n",
        "                  else:\n",
        "                    self.oilF[f][0]-=1\n",
        "                    self.oilF[f][1]=(self.oilF[f][0]-1)*8\n",
        "              else:\n",
        "                self.reward-=1\n",
        "          else:\n",
        "            self.reward-=3  \n",
        "          if self.oilF[(2,7)][0]==0 and self.oilF[(7,4)][0]==0:\n",
        "            self.done=True\n",
        "            break\n",
        "    elif action == 5:\n",
        "      self.count[5]+=1\n",
        "      temp={}\n",
        "      temp[abs(0-self.robotXs)+abs(0-self.robotYs)]=[0,0]\n",
        "      temp[abs(0-self.robotXs)+abs(9-self.robotYs)]=[0,9]\n",
        "      temp[abs(9-self.robotXs)+abs(0-self.robotYs)]=[9,0]\n",
        "      temp[abs(9-self.robotXs)+abs(9-self.robotYs)]=[9,9]\n",
        "      rew=min([abs(0-self.robotXs)+abs(0-self.robotYs),abs(0-self.robotXs)+abs(9-self.robotYs),abs(9-self.robotXs)+abs(0-self.robotYs),abs(9-self.robotXs)+abs(9-self.robotYs)])\n",
        "      self.reward-=rew\n",
        "      if rew==0:\n",
        "        self.reward-=1\n",
        "      self.reward+=self.robotLoad*10\n",
        "      self.robotXs=temp[rew][0]\n",
        "      self.robotYs=temp[rew][1]\n",
        "      self.robotLoad=0\n",
        "    return self.numStates[(self.robotLoad,self.robotXs,self.robotYs,self.oilF[(2,7)][0],self.oilF[(7,4)][0])],self.reward,self.done,\"non\"\n",
        "      "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "X4p6e0Nd8qq6"
      },
      "outputs": [],
      "source": [
        "env=OilF()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gcwFtJff8-TG",
        "outputId": "57f441a3-41e6-42ec-a82f-20d2bdd00ee6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are  3600  possible states\n",
            "There are  6  possible actions\n"
          ]
        }
      ],
      "source": [
        "state_space = len(env.states) # TODO\n",
        "print(\"There are \", state_space, \" possible states\")\n",
        "\n",
        "action_space = len(env.actions) # TODO\n",
        "print(\"There are \", action_space, \" possible actions\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "env.actions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NVzFMBU9Ber4",
        "outputId": "f4aada86-6b79-478e-9253-90372de5a5da"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 2, 3, 4, 5, 6]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "0BHsfiAW91pC"
      },
      "outputs": [],
      "source": [
        "# Let's create our Qtable of size (state_space, action_space) and initialized each values at 0 using np.zeros\n",
        "def initialize_q_table(state_space, action_space):\n",
        "  Qtable = np.zeros((state_space, action_space)) #TODO\n",
        "  return Qtable"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "D2ZjT0To93pk"
      },
      "outputs": [],
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JvAbic3Ckcrx",
        "outputId": "a19399d4-10cc-4cf4-a67f-a8cd005607f0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "Qtable_OilF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "cYxHuckr4LiG"
      },
      "outputs": [],
      "source": [
        "def epsilon_greedy_policy(Qtable, state, epsilon,env):\n",
        "  # Randomly generate a number between 0 and 1\n",
        "  random_int = random.uniform(0,1)\n",
        "  # if random_int > greater than epsilon --> exploitation\n",
        "  if random_int > epsilon:\n",
        "    # Take the action with the highest value given a state\n",
        "    # np.argmax can be useful here\n",
        "    action = np.argmax(Qtable[state])\n",
        "  # else --> exploration\n",
        "  else:\n",
        "    action = env.actionSpaceSample()\n",
        "  \n",
        "  return action"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def categorical_draw(probs):\n",
        "    z = random.random()\n",
        "    cum_prob = 0.0\n",
        "    \n",
        "    for i in range(len(probs)):\n",
        "        prob = probs[i]\n",
        "        cum_prob += prob\n",
        "        \n",
        "        if cum_prob > z:\n",
        "            return i\n",
        "    return len(probs) - 1"
      ],
      "metadata": {
        "id": "b9m7a7-5Ckbq"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def softmax_policy(Qtable,state,env,prob_t,temperature):\n",
        "  for a in range(len(env.actions)):\n",
        "    prob_t[a] = np.exp(Qtable[state][a]/temperature)  #calculate numerators\n",
        "\n",
        "#numpy matrix element-wise division for denominator (sum of numerators)\n",
        "  prob_t = np.true_divide(prob_t,sum(prob_t)) \n",
        "  return categorical_draw(prob_t)"
      ],
      "metadata": {
        "id": "JK5wUqx8-qmQ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "E3SCLmLX5bWG"
      },
      "outputs": [],
      "source": [
        "\n",
        "def greedy_policy(Qtable, state):\n",
        "  # Exploitation: take the action with the highest state, action value\n",
        "  action = np.argmax(Qtable[state]) # TODO\n",
        "  \n",
        "  return action"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "IyZaYbUAeolw"
      },
      "outputs": [],
      "source": [
        "def train_Q(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable,learning_rate,gamma):\n",
        "  episode_rewards=[]\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    total_rewards_ep=0\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action = epsilon_greedy_policy(Qtable, state, epsilon,env)\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "      # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n",
        "      Qtable[state][action] = Qtable[state][action] + learning_rate * (reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action])   \n",
        "      # If done, finish the episode\n",
        "      total_rewards_ep += reward\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our state is the new state\n",
        "      state = new_state\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "    \n",
        "  return Qtable,mean_reward,std_reward,episode_rewards"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "V0LydLuARk29"
      },
      "outputs": [],
      "source": [
        "def train_SARSA(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable,learning_rate,gamma):\n",
        "  episode_rewards=[]\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    action1 = epsilon_greedy_policy(Qtable, state, epsilon,env)\n",
        "    total_rewards_ep=0\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      new_state, reward, done, info = env.step(action1)\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action2 = epsilon_greedy_policy(Qtable, new_state, epsilon,env)\n",
        "\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      \n",
        "\n",
        "      # Update Q(s,a)\n",
        "      Qtable[state, action1] = Qtable[state, action1] + learning_rate * (reward + gamma * Qtable[new_state, action2] - Qtable[state, action1])\n",
        "\n",
        "      total_rewards_ep += reward\n",
        "      # If done, finish the episode\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our state is the new state\n",
        "      state = new_state\n",
        "      action1 = action2\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "  return Qtable,mean_reward,std_reward,episode_rewards"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "anQAcaqO1fxm"
      },
      "outputs": [],
      "source": [
        "def train_SQ(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable,learning_rate,gamma):\n",
        "  episode_rewards=[]\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    action1 = epsilon_greedy_policy(Qtable, state, epsilon,env)\n",
        "    total_rewards_ep=0\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "\n",
        "      if step<max_steps/2:\n",
        "        # Choose the action At using epsilon greedy policy\n",
        "        action = epsilon_greedy_policy(Qtable, state, epsilon,env)\n",
        "        # Take action At and observe Rt+1 and St+1\n",
        "        # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "        new_state, reward, done, info = env.step(action)\n",
        "        # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n",
        "        Qtable[state][action] = Qtable[state][action] + learning_rate * (reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action])   \n",
        "        # If done, finish the episode\n",
        "        total_rewards_ep += reward\n",
        "        if done:\n",
        "          break\n",
        "        \n",
        "        # Our state is the new state\n",
        "        state = new_state\n",
        "\n",
        "      else:\n",
        "        new_state, reward, done, info = env.step(action1)\n",
        "        # Choose the action At using epsilon greedy policy\n",
        "        action2 = epsilon_greedy_policy(Qtable, new_state, epsilon,env)\n",
        "\n",
        "        # Take action At and observe Rt+1 and St+1\n",
        "        # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "        \n",
        "\n",
        "        # Update Q(s,a)\n",
        "        Qtable[state, action1] = Qtable[state, action1] + learning_rate * (reward + gamma * Qtable[new_state, action2] - Qtable[state, action1])\n",
        "\n",
        "        total_rewards_ep += reward\n",
        "        # If done, finish the episode\n",
        "        if done:\n",
        "          break\n",
        "        \n",
        "        # Our state is the new state\n",
        "        state = new_state\n",
        "        action1 = action2\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "  return Qtable,mean_reward,std_reward,episode_rewards"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "BS_eCXjYxBhb"
      },
      "outputs": [],
      "source": [
        "def drawState(X,Y,L,cap1,cap2):\n",
        "  cord=(2,7),(7,4)\n",
        "  print(X,Y)\n",
        "  grid=[['3','0','0','0','0','0','0','0','0','3']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['0','0','0','0','0','0','0','0','0','0']\n",
        "       ,['3','0','0','0','0','0','0','0','0','3']]\n",
        "  for i in range(10):\n",
        "    for j in range(10):\n",
        "      if (abs(i-cord[0][0])<cap1 and abs(j-cord[0][1])<cap1)  or(abs(i-cord[1][0])<cap2 and abs(j-cord[1][1])<cap2):\n",
        "        grid[i][j]='1'\n",
        "        \n",
        "  grid[1][6]='2'\n",
        "  grid[8][5]='2'\n",
        "  grid[8][6]='2'\n",
        "  grid[8][7]='2'\n",
        "  grid[7][5]='2'\n",
        "  grid[7][6]='2'\n",
        "  grid[7][7]='2'\n",
        "  grid[9][5]='2'\n",
        "  grid[9][6]='2'\n",
        "  grid[9][7]='2'\n",
        "  if L==0:\n",
        "    grid[X][Y]='A'\n",
        "  elif L==1:\n",
        "    grid[X][Y]='B'\n",
        "  elif L==2:\n",
        "    grid[X][Y]='C'\n",
        "  elif L==3:\n",
        "    grid[X][Y]='D'\n",
        "  elif L==4:\n",
        "    grid[X][Y]='E'\n",
        "  elif L==5:\n",
        "    grid[X][Y]='F'\n",
        "  elif L==6:\n",
        "    grid[X][Y]='G'\n",
        "  elif L==7:\n",
        "    grid[X][Y]='H'\n",
        "  elif L==8:\n",
        "    grid[X][Y]='I'\n",
        "  elif L==9:\n",
        "    grid[X][Y]='J'\n",
        "  for i in range(10):\n",
        "    print(grid[i])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "10G5QJfT-tks"
      },
      "outputs": [],
      "source": [
        "def evaluate_agent(env, max_steps, n_eval_episodes, Q,draw=False):\n",
        "  \"\"\"\n",
        "  Evaluate the agent for ``n_eval_episodes`` episodes and returns average reward and std of reward.\n",
        "  :param env: The evaluation environment\n",
        "  :param n_eval_episodes: Number of episode to evaluate the agent\n",
        "  :param Q: The Q-table\n",
        "  \"\"\"\n",
        "  episode_rewards = []\n",
        "  for episode in tqdm(range(n_eval_episodes)):\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    total_rewards_ep = 0\n",
        "    \n",
        "    for step in range(max_steps):\n",
        "      # Take the action (index) that have the maximum expected future reward given that state\n",
        "      action = np.argmax(Q[state][:])\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "      total_rewards_ep += reward\n",
        "      if draw:\n",
        "        drawState(env.robotXs,env.robotYs,env.robotLoad,env.oilF[(2,7)][0],env.oilF[(7,4)][0])\n",
        "        time.sleep(0.5)\n",
        "        clear_output(wait=True)\n",
        "      if done:\n",
        "        break\n",
        "      state = new_state\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "\n",
        "  \n",
        "  return mean_reward, std_reward\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "SulMTT0tuYtB"
      },
      "outputs": [],
      "source": [
        "def visualize(mean_r,std_r,episode_rs):\n",
        "  #TODO\n",
        "\n",
        "  plt.plot( range(len(episode_rs)),episode_rs)\n",
        "  print(mean_r,std_r)\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
            "060f73f1f5c34e60b8db0f9e49f9cb39",
            "781dcbfa08cd4dc0a595e68301346803",
            "f9d2027577ad49039cbb7eb5bf876fca",
            "09c060785b974a60af4fdeff9bacd54f",
            "7bc747d4cd084a43819935605c773034",
            "c1177b0cc02a4c25a13ed07b64a9f7f8",
            "6538d45101e4424398fcae49b6c74c37",
            "465fe1a0ed8d4945a5bbc916edaff78d",
            "448fb4091dc9423a8df85fa078df8589",
            "cfb4d1418a9b48c798e13e2dab59a7f0",
            "e0c8bafba44d41b294f5dd48555bcc7e"
          ]
        },
        "id": "Gf1M2uJGuZpW",
        "outputId": "a0a92d9b-dc51-48b0-dda2-7abf8745d0cd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/6000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "060f73f1f5c34e60b8db0f9e49f9cb39"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-226.023 239.12669334685327\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)\n",
        "# Training parameters\n",
        "n_training_episodes = 6000  # Total training episodes\n",
        "learning_rate = 0.05         # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"OilF\"     # Name of the environment\n",
        "max_steps = 300               # Max steps per episode\n",
        "gamma = 0.998              # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.2         # Minimum exploration probability \n",
        "decay_rate = 0.0005            # Exponential decay rate for exploration prob\n",
        "Qtable_OilFN,mean_r,std_r,episode_rs2 = train_Q(int(n_training_episodes), min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_OilF,learning_rate,gamma)\n",
        "visualize(mean_r,std_r,episode_rs2)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward= evaluate_agent(env, max_steps, 1, Qtable_OilFN)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "a7ce4793fb294fa9bc76a42c4488765e",
            "ec98bec69d63426080e56ca1d9496122",
            "1673892a6fa84819a4a612f73af338e8",
            "f3a5cc77c8094f3bbdfe9c3c6b95ea44",
            "33e873175dff4b338f45b05d1a379039",
            "1e3ab3631d104fc093f8db55729fb916",
            "c35852cac59440a3b3dac430140bb542",
            "c04c31ebd2774cc3a7d99c18d5d9794c",
            "0842667eca244e868d0436b3d38534c9",
            "157e27af4b01499c897db8081759e09f",
            "ce6fc183245c4cfda13c326ff40a697c"
          ]
        },
        "id": "5J9k7j7ZoLSQ",
        "outputId": "5116f167-eab3-4e2e-dc26-70b00ca08d47"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a7ce4793fb294fa9bc76a42c4488765e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=159.00 +/- 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
            "5f6a5867ea8241bd8900a79879d01c37",
            "4dda2689ad7e4a619459a910f3724a52",
            "309bc27c2f91432aa343fac6e17e2260",
            "12790769a06d4f09b48ba23a64b68416",
            "d3a4734eaf4d4450aaa84e11bbad766f",
            "0a03d2847c6c4ca29cdfd49d6f02f578",
            "93c870ce4d1c4f24b705c9efc909c603",
            "bde7e0ac90d649f5b3bae604902b7e01",
            "13673702127a492face493c43e3fd29f",
            "4fe2b617ad934249b99b55a8c736905c",
            "798fe82cf26345b0a0c6f8c0d9f2b769"
          ]
        },
        "id": "y5nwhVTc48MZ",
        "outputId": "bfe57f41-864d-49b2-81ae-eff3f8e86537"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f6a5867ea8241bd8900a79879d01c37"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "35.293 259.5300852521726\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)\n",
        "# Training parameters\n",
        "n_training_episodes = 2000  # Total training episodes\n",
        "learning_rate = 0.05       # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"OilF\"     # Name of the environment\n",
        "max_steps = 300               # Max steps per episode\n",
        "gamma = 0.9999              # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05           # Minimum exploration probability \n",
        "decay_rate = 0.005            # Exponential decay rate for exploration prob\n",
        "Qtable_OilFN,mean_r,std_r,episode_rs1 = train_SARSA(int(n_training_episodes), min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_OilF,learning_rate,gamma)\n",
        "visualize(mean_r,std_r,episode_rs1)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward= evaluate_agent(env, max_steps, 1, Qtable_OilFN,True)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34,
          "referenced_widgets": [
            "fdd4475a39e54b3faf215fb58094d187",
            "9f5a5cd1fdcb4936b18fa7eaa4895d91",
            "123c520a8b034bd1bc62385a3593bdc7",
            "b51fd5eccd6948caafa10abd40725dc3",
            "290c75887368425c866323b49a0c36de",
            "a6d243912e1b412cadd28c4ac3aa1d67",
            "ffd950e837394e9b97fbbd883a405705",
            "e63240df170149999a89b4f71eb4be20",
            "bfd1fc66560041b7a8c3c711ea9b1dc4",
            "2e426cebd7414b76bc29f75ef8e81d46",
            "8765a561d95444f4afb2ef41e0996425"
          ]
        },
        "id": "osdNW4tIoQ-2",
        "outputId": "91d50d35-083c-44d4-f76d-66f938ba6060"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=231.00 +/- 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
            "04f18920db814363b2acb1ade7c2b7ad",
            "9b8d5634c3b54f09aea836508160a4a2",
            "75c8aac7be1a48ec8b7e7f28aace7dad",
            "ef73834fc3ab492eb63b35f98a2a1ebd",
            "d3d1307e971a4447b5b26d94537d6cf0",
            "9a4d284a526a404895ef60a852c7862f",
            "e1846f563d854976aa861607ee9f2383",
            "2b2b7f69ad1f476f85a7a8465e706624",
            "b794e8ee47854942b130769227a9e499",
            "2930bc1ca96641dd94367ee8f7d7efaa",
            "eb5e79d4f87d4591b70944b1aefa9f69"
          ]
        },
        "id": "Ovf457drvCWG",
        "outputId": "f9f3325b-2bdc-4470-d41b-51d6173da342"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/4000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "04f18920db814363b2acb1ade7c2b7ad"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-246.75825 261.21548347473106\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)\n",
        "# Training parameters\n",
        "n_training_episodes = 4000  # Total training episodes\n",
        "learning_rate = 0.05        # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"OilF\"     # Name of the environment\n",
        "max_steps = 300               # Max steps per episode\n",
        "gamma = 0.9999              # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05           # Minimum exploration probability \n",
        "decay_rate = 0.0005            # Exponential decay rate for exploration prob\n",
        "Qtable_OilFN,mean_r,std_r,episode_rs1 = train_SQ(int(n_training_episodes), min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_OilF,learning_rate,gamma)\n",
        "visualize(mean_r,std_r,episode_rs1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "6ab22d7225f644e8a26f5a202248f4cd",
            "0cd97ce2e9bb4d82a50b5a752b2d6aa3",
            "104e03f069704d3686a01d2de81925c8",
            "f4609a87f0ec4a37af045127032694c1",
            "c4d2ab7899e741d6a90c46ff293ac840",
            "9e2d13a7c9e2491ba2fe9e645b32cbd7",
            "62555455ac374780a9e2484f28ee626f",
            "7681cb67f2304bf4bdc91a78eb0d1da6",
            "27f81fcc09c24a81ab30c7b671bf6754",
            "6b9a037a65d442f19626f77b2a5018d4",
            "7065d67c586044e8a94a17497a826b7a"
          ]
        },
        "id": "qCfD_O4S-xxK",
        "outputId": "26498bd5-8e16-4549-c88e-a4715b505b2a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6ab22d7225f644e8a26f5a202248f4cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=231.00 +/- 0.00\n"
          ]
        }
      ],
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward= evaluate_agent(env, max_steps, 1, Qtable_OilFN)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_SARSA_soft(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable,learning_rate,gamma,temp):\n",
        "  episode_rewards=[]\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    prob_t = [0]* len(env.actions)\n",
        "    action1 = softmax_policy(Qtable,state,env,prob_t,temp)\n",
        "    total_rewards_ep=0\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      new_state, reward, done, info = env.step(action1)\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action2 = softmax_policy(Qtable,state,env,prob_t,temp)\n",
        "\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      \n",
        "\n",
        "      # Update Q(s,a)\n",
        "      Qtable[state, action1] = Qtable[state, action1] + learning_rate * (reward + gamma * Qtable[new_state, action2] - Qtable[state, action1])\n",
        "\n",
        "      total_rewards_ep += reward\n",
        "      # If done, finish the episode\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our state is the new state\n",
        "      state = new_state\n",
        "      action1 = action2\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "  return Qtable,mean_reward,std_reward,episode_rewards"
      ],
      "metadata": {
        "id": "EbRIIG0UDN4v"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)\n",
        "# Training parameters\n",
        "n_training_episodes = 6000  # Total training episodes\n",
        "learning_rate = 0.001   # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"OilF\"     # Name of the environment\n",
        "max_steps = 400               # Max steps per episode\n",
        "gamma = 0.9999              # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05           # Minimum exploration probability \n",
        "decay_rate = 0.01          # Exponential decay rate for exploration prob\n",
        "Qtable_OilFN,mean_r,std_r,episode_rs1 = train_SARSA_soft(int(n_training_episodes), min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_OilF,learning_rate,gamma,5)\n",
        "visualize(mean_r,std_r,episode_rs1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
            "d07d580741d14e5780b9cb8a5f3f8d07",
            "a119b235e7a34986827d3181624ceb49",
            "26eccdc74dcb47aca625a83bbd760584",
            "a3f6c29f3fb0426ab872a8032e002ba5",
            "326274849b4742a3baf6f3c206ca1911",
            "29795374f2174c4490a9eb462158c80e",
            "c3a7a4673a4541a898cbd26786714b2b",
            "3e885e302f544240bfd6d08d3c853651",
            "66fd37d50b41431babd14f76a1bc55fa",
            "201de9c2137c4de39a1ebb9c83491214",
            "b399d9323cbe4228898372df34a67d9a"
          ]
        },
        "id": "XBMlzPE5DN6q",
        "outputId": "e807d161-7729-4368-8637-439d173e5962"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/6000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d07d580741d14e5780b9cb8a5f3f8d07"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-841.0956666666667 78.03280409260938\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward= evaluate_agent(env, max_steps, 1, Qtable_OilFN)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "e1c2a235a19d460bb97402de51c84c50",
            "98451c6e26084607a3f0b928f800ddac",
            "6cb27eda758c4f64af75ca7fe7b007ba",
            "28d8aae9a8d14e8abfbe9b66f7b46a50",
            "92f98188a0c84829b5d44f0f4c7cebd2",
            "fdf8116143cb4d3a8720aa101514ef3f",
            "c6d9ef70e613426f918149972533709b",
            "6a62d4de529f48b9889698b2ec28d7af",
            "b3dd7c42211e405b92bba50c895fcc29",
            "5a4136de32294997b1c0e25f95953836",
            "42a37340c4f543dd843d8d940bdbad16"
          ]
        },
        "id": "z8CwK0G9owoT",
        "outputId": "187156f6-9c55-40ed-9980-9e1971048434"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e1c2a235a19d460bb97402de51c84c50"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=-691.00 +/- 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_Q_soft(n_training_episodes, min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable,learning_rate,gamma,temp):\n",
        "  episode_rewards=[]\n",
        "  for episode in tqdm(range(n_training_episodes)):\n",
        "    # Reduce epsilon (because we need less and less exploration)\n",
        "    epsilon = min_epsilon + (max_epsilon - min_epsilon)*np.exp(-decay_rate*episode)\n",
        "    # Reset the environment\n",
        "    state = env.reset()\n",
        "    step = 0\n",
        "    done = False\n",
        "    prob_t = [0]* len(env.actions)\n",
        "    total_rewards_ep=0\n",
        "\n",
        "    # repeat\n",
        "    for step in range(max_steps):\n",
        "      # Choose the action At using epsilon greedy policy\n",
        "      action = softmax_policy(Qtable,state,env,prob_t,temp)\n",
        "      # Take action At and observe Rt+1 and St+1\n",
        "      # Take the action (a) and observe the outcome state(s') and reward (r)\n",
        "      new_state, reward, done, info = env.step(action)\n",
        "      # Update Q(s,a):= Q(s,a) + lr [R(s,a) + gamma * max Q(s',a') - Q(s,a)]\n",
        "      Qtable[state][action] = Qtable[state][action] + learning_rate * (reward + gamma * np.max(Qtable[new_state]) - Qtable[state][action])   \n",
        "      # If done, finish the episode\n",
        "      total_rewards_ep += reward\n",
        "      if done:\n",
        "        break\n",
        "      \n",
        "      # Our state is the new state\n",
        "      state = new_state\n",
        "    episode_rewards.append(total_rewards_ep)\n",
        "  mean_reward = np.mean(episode_rewards)\n",
        "  std_reward = np.std(episode_rewards)\n",
        "    \n",
        "  return Qtable,mean_reward,std_reward,episode_rewards"
      ],
      "metadata": {
        "id": "vwyWGmPJDN8n"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Qtable_OilF = initialize_q_table(state_space, action_space)\n",
        "# Training parameters\n",
        "n_training_episodes = 6000  # Total training episodes\n",
        "learning_rate = 0.001   # Learning rate\n",
        "\n",
        "# Environment parameters\n",
        "env_id = \"OilF\"     # Name of the environment\n",
        "max_steps = 400               # Max steps per episode\n",
        "gamma = 0.9999              # Discounting rate\n",
        "\n",
        "# Exploration parameters\n",
        "max_epsilon = 1.0             # Exploration probability at start\n",
        "min_epsilon = 0.05           # Minimum exploration probability \n",
        "decay_rate = 0.005        # Exponential decay rate for exploration prob\n",
        "Qtable_OilFN,mean_r,std_r,episode_rs1 = train_Q_soft(int(n_training_episodes), min_epsilon, max_epsilon, decay_rate, env, max_steps, Qtable_OilF,learning_rate,gamma,0.85)\n",
        "visualize(mean_r,std_r,episode_rs1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314,
          "referenced_widgets": [
            "f0ebf98b12e04b3095bd8a31f03f206f",
            "5731cf6094c848f385e9d9d6c0d63c5b",
            "164de32fd275451d9a04aa028364e621",
            "8ecfbea490104b00b6ffaea24342e9f9",
            "fdde9256b38145c3903d49bcf54307be",
            "6346a7eb4bd948b1bc7a7a0ebeb75da1",
            "a4916620a3974d4c84a887e53eb3f297",
            "88d2dc2325d343779f65ad85267747b1",
            "cc92d9ce2b744709a827a68ac4ab0228",
            "845233662d124897b38f081a64f76c38",
            "b3369c8821b1439c8013ede61fcac323"
          ]
        },
        "id": "c4w4QDgnToD7",
        "outputId": "67001fe8-c8b1-41c9-fa20-38c67ba83717"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/6000 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0ebf98b12e04b3095bd8a31f03f206f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-673.9461666666666 117.11752901240797\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate our Agent\n",
        "mean_reward, std_reward= evaluate_agent(env, max_steps, 1, Qtable_OilFN)\n",
        "print(f\"Mean_reward={mean_reward:.2f} +/- {std_reward:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "366d50cce83447f7b6d56ab64c73999e",
            "f9ff20e41d5244c2a3e54c77ab22db23",
            "b117549cbfa84558880306f38c62c310",
            "22fca10cb54640108716968550b24cbe",
            "59d12bc2d6d84c829df3e5996628b620",
            "8a37b41a8a50476ea895cdbb5691376e",
            "be2c26930b24422281cd094baec4f480",
            "b7c99a8c44844badb1db29eedd753a66",
            "69b32d419c0b4e33b748bd7befa41be7",
            "abe0871462d64f87850d6fc67d7c719f",
            "9d5023881f72410db16d3aa7c96ecbf7"
          ]
        },
        "id": "MYvvaw6Mo1i4",
        "outputId": "bf75f415-0693-4c40-9dad-0438b5e292c1"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "366d50cce83447f7b6d56ab64c73999e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean_reward=-474.00 +/- 0.00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2Hc8PFbpo11n"
      },
      "execution_count": 30,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "060f73f1f5c34e60b8db0f9e49f9cb39": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_781dcbfa08cd4dc0a595e68301346803",
              "IPY_MODEL_f9d2027577ad49039cbb7eb5bf876fca",
              "IPY_MODEL_09c060785b974a60af4fdeff9bacd54f"
            ],
            "layout": "IPY_MODEL_7bc747d4cd084a43819935605c773034"
          }
        },
        "781dcbfa08cd4dc0a595e68301346803": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c1177b0cc02a4c25a13ed07b64a9f7f8",
            "placeholder": "​",
            "style": "IPY_MODEL_6538d45101e4424398fcae49b6c74c37",
            "value": "100%"
          }
        },
        "f9d2027577ad49039cbb7eb5bf876fca": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
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            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
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